机器学习模型在用MRI进行化学栓塞后预测治疗反应 临床放射学特征
Okan İnce1, Hakan Önder2, Mehmet Gençtürk3
1Department of Radiology, Medical School, University of Minnesota, 420 Delaware Street S.E, Minneapolis, MN, 55455, USA. okan_ince@yahoo.com.
Cardiovascular and interventional radiology
|October 26, 2023
概括
使用放射学和临床数据的机器学习模型显示,在肝细胞癌 (HCC) 经过过动脉化学栓塞 (TACE) 后预测局部瘤反应方面具有前景. 整合临床特征显著改善了预测治疗结果的模型性能.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 肝细胞癌 (HCC) 是一个主要的全球健康问题.
- 跨动脉化学栓塞 (TACE) 是HCC的标准治疗方法.
- 预测TACE的治疗反应对于患者管理至关重要.
研究的目的:
- 评估机器学习模型在TACE后预测局部瘤响应方面的有效性.
- 评估来自MRI的放射学和临床放射学特征的预测价值.
- 为了确定临床数据与成像特征相结合的额外好处.
主要方法:
- 对接受TACE的188名先前未接受治疗的HCC患者进行了回顾性分析.
- 从前对比T1加权成像 (T1WI) 和对比增强T1 (CE-T1) MRI中提取放射性特征.
- 使用放射学和临床数据开发支持向量机 (SVM) 模型.
- 使用顺序特征选择 (SFS) 算法进行特征选择.
主要成果:
- 支持矢量机 (SVM) 模型实现了曲线下的面积 (AUC) 高达0.91.
- 放射学和临床特征的综合数据集显示出卓越的预测性能.
- 包括临床特征显著改善了模型性能 (p=0.001).
- 较高的白细胞 (WBC) 和中性粒细胞计数与较低的治疗反应有关.
结论:
- 结合临床和MRI放射学特征的机器学习模型显示,TACE后预测局部反应的潜力.
- 这些模型可能有助于优化TACE接受HCC患者的治疗策略.
- 需要在更大的队列中进一步验证.
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